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15 results on '"Grotegerd, D"'

1. Functional connectivity signatures of major depressive disorder: machine learning analysis of two multicenter neuroimaging studies

Author

Gallo, S, El-Gazzar, A, Zhutovsky, P, Thomas, RM, Javaheripour, N, Li, M, Bartova, L, Bathula, D, Dannlowski, U, Davey, C, Frodl, T, Gotlib, I, Grimm, S, Grotegerd, D, Hahn, T, Hamilton, PJ, Harrison, BJ, Jansen, A, Kircher, T, Meyer, B, Nenadic, I, Olbrich, S, Paul, E, Pezawas, L, Sacchet, MD, Saemann, P, Wagner, G, Walter, H, Walter, M, van Wingen, G, Gallo, S, El-Gazzar, A, Zhutovsky, P, Thomas, RM, Javaheripour, N, Li, M, Bartova, L, Bathula, D, Dannlowski, U, Davey, C, Frodl, T, Gotlib, I, Grimm, S, Grotegerd, D, Hahn, T, Hamilton, PJ, Harrison, BJ, Jansen, A, Kircher, T, Meyer, B, Nenadic, I, Olbrich, S, Paul, E, Pezawas, L, Sacchet, MD, Saemann, P, Wagner, G, Walter, H, Walter, M, and van Wingen, G

Abstract

The promise of machine learning has fueled the hope for developing diagnostic tools for psychiatry. Initial studies showed high accuracy for the identification of major depressive disorder (MDD) with resting-state connectivity, but progress has been hampered by the absence of large datasets. Here we used regular machine learning and advanced deep learning algorithms to differentiate patients with MDD from healthy controls and identify neurophysiological signatures of depression in two of the largest resting-state datasets for MDD. We obtained resting-state functional magnetic resonance imaging data from the REST-meta-MDD (N = 2338) and PsyMRI (N = 1039) consortia. Classification of functional connectivity matrices was done using support vector machines (SVM) and graph convolutional neural networks (GCN), and performance was evaluated using 5-fold cross-validation. Features were visualized using GCN-Explainer, an ablation study and univariate t-testing. The results showed a mean classification accuracy of 61% for MDD versus controls. Mean accuracy for classifying (non-)medicated subgroups was 62%. Sex classification accuracy was substantially better across datasets (73-81%). Visualization of the results showed that classifications were driven by stronger thalamic connections in both datasets, while nearly all other connections were weaker with small univariate effect sizes. These results suggest that whole brain resting-state connectivity is a reliable though poor biomarker for MDD, presumably due to disease heterogeneity as further supported by the higher accuracy for sex classification using the same methods. Deep learning revealed thalamic hyperconnectivity as a prominent neurophysiological signature of depression in both multicenter studies, which may guide the development of biomarkers in future studies.

Published
2023

2. Genome-wide interaction study with major depression identifies novel variants associated with cognitive function

Author

Thalamuthu, A, Mills, NT, Berger, K, Minnerup, H, Grotegerd, D, Dannlowski, U, Meinert, S, Opel, N, Repple, J, Gruber, M, Stein, F, Brosch, K, Meller, T, Pfarr, J-K, Forstner, AJ, Hoffmann, P, Nothen, MM, Witt, S, Rietschel, M, Kircher, T, Adams, M, McIntosh, AM, Porteous, DJ, Deary, IJ, Hayward, C, Campbell, A, Grabe, HJ, Teumer, A, Homuth, G, Van der Auwera-Palitschka, S, Schubert, KO, Baune, BT, Thalamuthu, A, Mills, NT, Berger, K, Minnerup, H, Grotegerd, D, Dannlowski, U, Meinert, S, Opel, N, Repple, J, Gruber, M, Stein, F, Brosch, K, Meller, T, Pfarr, J-K, Forstner, AJ, Hoffmann, P, Nothen, MM, Witt, S, Rietschel, M, Kircher, T, Adams, M, McIntosh, AM, Porteous, DJ, Deary, IJ, Hayward, C, Campbell, A, Grabe, HJ, Teumer, A, Homuth, G, Van der Auwera-Palitschka, S, Schubert, KO, and Baune, BT

Abstract

Major Depressive Disorder (MDD) often is associated with significant cognitive dysfunction. We conducted a meta-analysis of genome-wide interaction of MDD and cognitive function using data from four large European cohorts in a total of 3510 MDD cases and 6057 controls. In addition, we conducted analyses using polygenic risk scores (PRS) based on data from the Psychiatric Genomics Consortium (PGC) on the traits of MDD, Bipolar disorder (BD), Schizophrenia (SCZ), and mood instability (MIN). Functional exploration contained gene expression analyses and Ingenuity Pathway Analysis (IPA®). We identified a set of significantly interacting single nucleotide polymorphisms (SNPs) between MDD and the genome-wide association study (GWAS) of cognitive domains of executive function, processing speed, and global cognition. Several of these SNPs are located in genes expressed in brain, with important roles such as neuronal development (REST), oligodendrocyte maturation (TNFRSF21), and myelination (ARFGEF1). IPA® identified a set of core genes from our dataset that mapped to a wide range of canonical pathways and biological functions (MPO, FOXO1, PDE3A, TSLP, NLRP9, ADAMTS5, ROBO1, REST). Furthermore, IPA® identified upstream regulator molecules and causal networks impacting on the expression of dataset genes, providing a genetic basis for further clinical exploration (vitamin D receptor, beta-estradiol, tadalafil). PRS of MIN and meta-PRS of MDD, MIN and SCZ were significantly associated with all cognitive domains. Our results suggest several genes involved in physiological processes for the development and maintenance of cognition in MDD, as well as potential novel therapeutic agents that could be explored in patients with MDD associated cognitive dysfunction.

Published
2022

3. Cortical and subcortical neuroanatomical signatures of schizotypy in 3004 individuals assessed in a worldwide ENIGMA study

Author

Kirschner, M, Hodzic-Santor, B, Antoniades, M, Nenadic, I, Kircher, T, Krug, A, Meller, T, Grotegerd, D, Fornito, A, Arnatkeviciute, A, Bellgrove, MA, Tiego, J, Dannlowski, U, Koch, K, Huelsmann, C, Kugel, H, Enneking, V, Klug, M, Leehr, EJ, Boehnlein, J, Gruber, M, Mehler, D, DeRosse, P, Moyett, A, Baune, BT, Green, M, Quide, Y, Pantelis, C, Chan, R, Wang, Y, Ettinger, U, Debbane, M, Derome, M, Gaser, C, Besteher, B, Diederen, K, Spencer, TJ, Fletcher, P, Roessler, W, Smigielski, L, Kumari, V, Premkumar, P, Park, HRP, Wiebels, K, Lemmers-Jansen, I, Gilleen, J, Allen, P, Kozhuharova, P, Marsman, J-B, Lebedeva, I, Tomyshev, A, Mukhorina, A, Kaiser, S, Fett, A-K, Sommer, I, Schuite-Koops, S, Paquola, C, Lariviere, S, Bernhardt, B, Dagher, A, Grant, P, van Erp, TGM, Turner, JA, Thompson, PM, Aleman, A, Modinos, G, Kirschner, M, Hodzic-Santor, B, Antoniades, M, Nenadic, I, Kircher, T, Krug, A, Meller, T, Grotegerd, D, Fornito, A, Arnatkeviciute, A, Bellgrove, MA, Tiego, J, Dannlowski, U, Koch, K, Huelsmann, C, Kugel, H, Enneking, V, Klug, M, Leehr, EJ, Boehnlein, J, Gruber, M, Mehler, D, DeRosse, P, Moyett, A, Baune, BT, Green, M, Quide, Y, Pantelis, C, Chan, R, Wang, Y, Ettinger, U, Debbane, M, Derome, M, Gaser, C, Besteher, B, Diederen, K, Spencer, TJ, Fletcher, P, Roessler, W, Smigielski, L, Kumari, V, Premkumar, P, Park, HRP, Wiebels, K, Lemmers-Jansen, I, Gilleen, J, Allen, P, Kozhuharova, P, Marsman, J-B, Lebedeva, I, Tomyshev, A, Mukhorina, A, Kaiser, S, Fett, A-K, Sommer, I, Schuite-Koops, S, Paquola, C, Lariviere, S, Bernhardt, B, Dagher, A, Grant, P, van Erp, TGM, Turner, JA, Thompson, PM, Aleman, A, and Modinos, G

Abstract

Neuroanatomical abnormalities have been reported along a continuum from at-risk stages, including high schizotypy, to early and chronic psychosis. However, a comprehensive neuroanatomical mapping of schizotypy remains to be established. The authors conducted the first large-scale meta-analyses of cortical and subcortical morphometric patterns of schizotypy in healthy individuals, and compared these patterns with neuroanatomical abnormalities observed in major psychiatric disorders. The sample comprised 3004 unmedicated healthy individuals (12-68 years, 46.5% male) from 29 cohorts of the worldwide ENIGMA Schizotypy working group. Cortical and subcortical effect size maps with schizotypy scores were generated using standardized methods. Pattern similarities were assessed between the schizotypy-related cortical and subcortical maps and effect size maps from comparisons of schizophrenia (SZ), bipolar disorder (BD) and major depression (MDD) patients with controls. Thicker right medial orbitofrontal/ventromedial prefrontal cortex (mOFC/vmPFC) was associated with higher schizotypy scores (r = 0.067, pFDR = 0.02). The cortical thickness profile in schizotypy was positively correlated with cortical abnormalities in SZ (r = 0.285, pspin = 0.024), but not BD (r = 0.166, pspin = 0.205) or MDD (r = -0.274, pspin = 0.073). The schizotypy-related subcortical volume pattern was negatively correlated with subcortical abnormalities in SZ (rho = -0.690, pspin = 0.006), BD (rho = -0.672, pspin = 0.009), and MDD (rho = -0.692, pspin = 0.004). Comprehensive mapping of schizotypy-related brain morphometry in the general population revealed a significant relationship between higher schizotypy and thicker mOFC/vmPFC, in the absence of confounding effects due to antipsychotic medication or disease chronicity. The cortical pattern similarity between schizotypy and schizophrenia yields new insights into a dimensional neurobiological continuity across the extended psychosis phenotype.

Published
2022

4. Structural brain alterations associated with suicidal thoughts and behaviors in young people: results from 21 international studies from the ENIGMA Suicidal Thoughts and Behaviours consortium

Author

van Velzen, LS, Dauvermann, MR, Colic, L, Villa, LM, Savage, HS, Toenders, YJ, Zhu, AH, Bright, JK, Campos, A, Salminen, LE, Ambrogi, S, Ayesa-Arriola, R, Banaj, N, Basgoze, Z, Bauer, J, Blair, K, Blair, RJ, Brosch, K, Cheng, Y, Colle, R, Connolly, CG, Corruble, E, Couvy-Duchesne, B, Crespo-Facorro, B, Cullen, KR, Dannlowski, U, Davey, CG, Dohm, K, Fullerton, JM, Gonul, AS, Gotlib, IH, Grotegerd, D, Hahn, T, Harrison, BJ, He, M, Hickie, IB, Ho, TC, Iorfino, F, Jansen, A, Jollant, F, Kircher, T, Klimes-Dougan, B, Klug, M, Leehr, EJ, Lippard, ETC, McLaughlin, KA, Meinert, S, Miller, AB, Mitchell, PB, Mwangi, B, Nenadic, I, Ojha, A, Overs, BJ, Pfarr, J-K, Piras, F, Ringwald, KG, Roberts, G, Romer, G, Sanches, M, Sheridan, MA, Soares, JC, Spalletta, G, Stein, F, Teresi, G, Tordesillas-Gutierrez, D, Uyar-Demir, A, van der Wee, NJA, van der Werff, SJ, Vermeiren, RRJM, Winter, A, Wu, M-J, Yang, TT, Thompson, PM, Renteria, ME, Jahanshad, N, Blumberg, HP, Van Harmelen, A-L, Schmaal, L, van Velzen, LS, Dauvermann, MR, Colic, L, Villa, LM, Savage, HS, Toenders, YJ, Zhu, AH, Bright, JK, Campos, A, Salminen, LE, Ambrogi, S, Ayesa-Arriola, R, Banaj, N, Basgoze, Z, Bauer, J, Blair, K, Blair, RJ, Brosch, K, Cheng, Y, Colle, R, Connolly, CG, Corruble, E, Couvy-Duchesne, B, Crespo-Facorro, B, Cullen, KR, Dannlowski, U, Davey, CG, Dohm, K, Fullerton, JM, Gonul, AS, Gotlib, IH, Grotegerd, D, Hahn, T, Harrison, BJ, He, M, Hickie, IB, Ho, TC, Iorfino, F, Jansen, A, Jollant, F, Kircher, T, Klimes-Dougan, B, Klug, M, Leehr, EJ, Lippard, ETC, McLaughlin, KA, Meinert, S, Miller, AB, Mitchell, PB, Mwangi, B, Nenadic, I, Ojha, A, Overs, BJ, Pfarr, J-K, Piras, F, Ringwald, KG, Roberts, G, Romer, G, Sanches, M, Sheridan, MA, Soares, JC, Spalletta, G, Stein, F, Teresi, G, Tordesillas-Gutierrez, D, Uyar-Demir, A, van der Wee, NJA, van der Werff, SJ, Vermeiren, RRJM, Winter, A, Wu, M-J, Yang, TT, Thompson, PM, Renteria, ME, Jahanshad, N, Blumberg, HP, Van Harmelen, A-L, and Schmaal, L

Abstract

Identifying brain alterations associated with suicidal thoughts and behaviors (STBs) in young people is critical to understanding their development and improving early intervention and prevention. The ENIGMA Suicidal Thoughts and Behaviours (ENIGMA-STB) consortium analyzed neuroimaging data harmonized across sites to examine brain morphology associated with STBs in youth. We performed analyses in three separate stages, in samples ranging from most to least homogeneous in terms of suicide assessment instrument and mental disorder. First, in a sample of 577 young people with mood disorders, in which STBs were assessed with the Columbia Suicide Severity Rating Scale (C-SSRS). Second, in a sample of young people with mood disorders, in which STB were assessed using different instruments, MRI metrics were compared among healthy controls without STBs (HC; N = 519), clinical controls with a mood disorder but without STBs (CC; N = 246) and young people with current suicidal ideation (N = 223). In separate analyses, MRI metrics were compared among HCs (N = 253), CCs (N = 217), and suicide attempters (N = 64). Third, in a larger transdiagnostic sample with various assessment instruments (HC = 606; CC = 419; Ideation = 289; HC = 253; CC = 432; Attempt=91). In the homogeneous C-SSRS sample, surface area of the frontal pole was lower in young people with mood disorders and a history of actual suicide attempts (N = 163) than those without a lifetime suicide attempt (N = 323; FDR-p = 0.035, Cohen's d = 0.34). No associations with suicidal ideation were found. When examining more heterogeneous samples, we did not observe significant associations. Lower frontal pole surface area may represent a vulnerability for a (non-interrupted and non-aborted) suicide attempt; however, more research is needed to understand the nature of its relationship to suicide risk.

Published
2022

5. Brain structural abnormalities in obesity: relation to age, genetic risk, and common psychiatric disorders (May, 2020, 10.1038/s41380-020-0774-9)

Author

Opel, N, Thalamuthu, A, Milaneschi, Y, Grotegerd, D, Flint, C, Leenings, R, Goltermann, J, Richter, M, Hahn, T, Woditsch, G, Berger, K, Hermesdorf, M, McIntosh, A, Whalley, HC, Harris, MA, MacMaster, FP, Walter, H, Veer, IM, Frodl, T, Carballedo, A, Krug, A, Nenadic, I, Kircher, T, Aleman, A, Groenewold, NA, Stein, DJ, Soares, JC, Zunta-Soares, GB, Mwangi, B, Wu, M-J, Walter, M, Li, M, Harrison, BJ, Davey, CG, Cullen, KR, Klimes-Dougan, B, Mueller, BA, Samann, PG, Penninx, B, Nawijn, L, Veltman, DJ, Aftanas, L, Brak, IV, Filimonova, EA, Osipov, EA, Reneman, L, Schrantee, A, Grabe, HJ, van der Auwera, S, Wittfeld, K, Hosten, N, Volzke, H, Sim, K, Gotlib, IH, Sacchet, MD, Lagopoulos, J, Hatton, SN, Hickie, I, Pozzi, E, Thompson, PM, Jahanshad, N, Schmaal, L, Baune, BT, Dannlowski, U, Opel, N, Thalamuthu, A, Milaneschi, Y, Grotegerd, D, Flint, C, Leenings, R, Goltermann, J, Richter, M, Hahn, T, Woditsch, G, Berger, K, Hermesdorf, M, McIntosh, A, Whalley, HC, Harris, MA, MacMaster, FP, Walter, H, Veer, IM, Frodl, T, Carballedo, A, Krug, A, Nenadic, I, Kircher, T, Aleman, A, Groenewold, NA, Stein, DJ, Soares, JC, Zunta-Soares, GB, Mwangi, B, Wu, M-J, Walter, M, Li, M, Harrison, BJ, Davey, CG, Cullen, KR, Klimes-Dougan, B, Mueller, BA, Samann, PG, Penninx, B, Nawijn, L, Veltman, DJ, Aftanas, L, Brak, IV, Filimonova, EA, Osipov, EA, Reneman, L, Schrantee, A, Grabe, HJ, van der Auwera, S, Wittfeld, K, Hosten, N, Volzke, H, Sim, K, Gotlib, IH, Sacchet, MD, Lagopoulos, J, Hatton, SN, Hickie, I, Pozzi, E, Thompson, PM, Jahanshad, N, Schmaal, L, Baune, BT, and Dannlowski, U

Published
2021

6. Brain structural abnormalities in obesity: relation to age, genetic risk, and common psychiatric disorders Evidence through univariate and multivariate mega-analysis including 6420 participants from the ENIGMA MDD working group

Author

Opel, N, Thalamuthu, A, Milaneschi, Y, Grotegerd, D, Flint, C, Leenings, R, Goltermann, J, Richter, M, Hahn, T, Woditsch, G, Berger, K, Hermesdorf, M, McIntosh, A, Whalley, HC, Harris, MA, MacMaster, FP, Walter, H, Veer, IM, Frodl, T, Carballedo, A, Krug, A, Nenadic, I, Kircher, T, Aleman, A, Groenewold, NA, Stein, DJ, Soares, JC, Zunta-Soares, GB, Mwangi, B, Wu, M-J, Walter, M, Li, M, Harrison, BJ, Davey, CG, Cullen, KR, Klimes-Dougan, B, Mueller, BA, Saemann, PG, Penninx, B, Nawijn, L, Veltman, DJ, Aftanas, L, Brak, I, Filimonova, EA, Osipov, EA, Reneman, L, Schrantee, A, Grabe, HJ, Van der Auwera, S, Wittfeld, K, Hosten, N, Voelzke, H, Sim, K, Gotlib, IH, Sacchet, MD, Lagopoulos, J, Hatton, SN, Hickie, I, Pozzi, E, Thompson, PM, Jahanshad, N, Schmaal, L, Baune, BT, Dannlowski, U, Opel, N, Thalamuthu, A, Milaneschi, Y, Grotegerd, D, Flint, C, Leenings, R, Goltermann, J, Richter, M, Hahn, T, Woditsch, G, Berger, K, Hermesdorf, M, McIntosh, A, Whalley, HC, Harris, MA, MacMaster, FP, Walter, H, Veer, IM, Frodl, T, Carballedo, A, Krug, A, Nenadic, I, Kircher, T, Aleman, A, Groenewold, NA, Stein, DJ, Soares, JC, Zunta-Soares, GB, Mwangi, B, Wu, M-J, Walter, M, Li, M, Harrison, BJ, Davey, CG, Cullen, KR, Klimes-Dougan, B, Mueller, BA, Saemann, PG, Penninx, B, Nawijn, L, Veltman, DJ, Aftanas, L, Brak, I, Filimonova, EA, Osipov, EA, Reneman, L, Schrantee, A, Grabe, HJ, Van der Auwera, S, Wittfeld, K, Hosten, N, Voelzke, H, Sim, K, Gotlib, IH, Sacchet, MD, Lagopoulos, J, Hatton, SN, Hickie, I, Pozzi, E, Thompson, PM, Jahanshad, N, Schmaal, L, Baune, BT, and Dannlowski, U

Abstract

Emerging evidence suggests that obesity impacts brain physiology at multiple levels. Here we aimed to clarify the relationship between obesity and brain structure using structural MRI (n = 6420) and genetic data (n = 3907) from the ENIGMA Major Depressive Disorder (MDD) working group. Obesity (BMI > 30) was significantly associated with cortical and subcortical abnormalities in both mass-univariate and multivariate pattern recognition analyses independent of MDD diagnosis. The most pronounced effects were found for associations between obesity and lower temporo-frontal cortical thickness (maximum Cohen´s d (left fusiform gyrus) = -0.33). The observed regional distribution and effect size of cortical thickness reductions in obesity revealed considerable similarities with corresponding patterns of lower cortical thickness in previously published studies of neuropsychiatric disorders. A higher polygenic risk score for obesity significantly correlated with lower occipital surface area. In addition, a significant age-by-obesity interaction on cortical thickness emerged driven by lower thickness in older participants. Our findings suggest a neurobiological interaction between obesity and brain structure under physiological and pathological brain conditions.

Published
2021

7. Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group

Author

Han, LKM, Dinga, R, Hahn, T, Ching, CRK, Eyler, LT, Aftanas, L, Aghajani, M, Aleman, A, Baune, BT, Berger, K, Brak, I, Busatto Filho, G, Carballedo, A, Connolly, CG, Couvy-Duchesne, B, Cullen, KR, Dannlowski, U, Davey, CG, Dima, D, Duran, FLS, Enneking, V, Filimonova, E, Frenzel, S, Frodl, T, Fu, CHY, Godlewska, BR, Gotlib, IH, Grabe, HJ, Groenewold, NA, Grotegerd, D, Gruber, O, Hall, GB, Harrison, BJ, Hatton, SN, Hermesdorf, M, Hickie, IB, Ho, TC, Hosten, N, Jansen, A, Kaehler, C, Kircher, T, Klimes-Dougan, B, Kraemer, B, Krug, A, Lagopoulos, J, Leenings, R, MacMaster, FP, MacQueen, G, McIntosh, A, McLellan, Q, McMahon, KL, Medland, SE, Mueller, BA, Mwangi, B, Osipov, E, Portella, MJ, Pozzi, E, Reneman, L, Repple, J, Rosa, PGP, Sacchet, MD, Saemann, PG, Schnell, K, Schrantee, A, Simulionyte, E, Soares, JC, Sommer, J, Stein, DJ, Steinstraeter, O, Strike, LT, Thomopoulos, SI, van Tol, M-J, Veer, IM, Vermeiren, RRJM, Walter, H, van der Wee, NJA, van der Werff, SJA, Whalley, H, Winter, NR, Wittfeld, K, Wright, MJ, Wu, M-J, Voelzke, H, Yang, TT, Zannias, V, de Zubicaray, GI, Zunta-Soares, GB, Abe, C, Alda, M, Andreassen, OA, Boen, E, Bonnin, CM, Canales-Rodriguez, EJ, Cannon, D, Caseras, X, Chaim-Avancini, TM, Elvsashagen, T, Favre, P, Foley, SF, Fullerton, JM, Goikolea, JM, Haarman, BCM, Hajek, T, Henry, C, Houenou, J, Howells, FM, Ingvar, M, Kuplicki, R, Lafer, B, Landen, M, Machado-Vieira, R, Malt, UF, McDonald, C, Mitchell, PB, Nabulsi, L, Otaduy, MCG, Overs, BJ, Polosan, M, Pomarol-Clotet, E, Radua, J, Rive, MM, Roberts, G, Ruhe, HG, Salvador, R, Sarro, S, Satterthwaite, TD, Savitz, J, Schene, AH, Schofield, PR, Serpa, MH, Sim, K, Soeiro-de-Souza, MG, Sutherland, AN, Temmingh, HS, Timmons, GM, Uhlmann, A, Vieta, E, Wolf, DH, Zanetti, MV, Jahanshad, N, Thompson, PM, Veltman, DJ, Penninx, BWJH, Marquand, AF, Cole, JH, Schmaal, L, Han, LKM, Dinga, R, Hahn, T, Ching, CRK, Eyler, LT, Aftanas, L, Aghajani, M, Aleman, A, Baune, BT, Berger, K, Brak, I, Busatto Filho, G, Carballedo, A, Connolly, CG, Couvy-Duchesne, B, Cullen, KR, Dannlowski, U, Davey, CG, Dima, D, Duran, FLS, Enneking, V, Filimonova, E, Frenzel, S, Frodl, T, Fu, CHY, Godlewska, BR, Gotlib, IH, Grabe, HJ, Groenewold, NA, Grotegerd, D, Gruber, O, Hall, GB, Harrison, BJ, Hatton, SN, Hermesdorf, M, Hickie, IB, Ho, TC, Hosten, N, Jansen, A, Kaehler, C, Kircher, T, Klimes-Dougan, B, Kraemer, B, Krug, A, Lagopoulos, J, Leenings, R, MacMaster, FP, MacQueen, G, McIntosh, A, McLellan, Q, McMahon, KL, Medland, SE, Mueller, BA, Mwangi, B, Osipov, E, Portella, MJ, Pozzi, E, Reneman, L, Repple, J, Rosa, PGP, Sacchet, MD, Saemann, PG, Schnell, K, Schrantee, A, Simulionyte, E, Soares, JC, Sommer, J, Stein, DJ, Steinstraeter, O, Strike, LT, Thomopoulos, SI, van Tol, M-J, Veer, IM, Vermeiren, RRJM, Walter, H, van der Wee, NJA, van der Werff, SJA, Whalley, H, Winter, NR, Wittfeld, K, Wright, MJ, Wu, M-J, Voelzke, H, Yang, TT, Zannias, V, de Zubicaray, GI, Zunta-Soares, GB, Abe, C, Alda, M, Andreassen, OA, Boen, E, Bonnin, CM, Canales-Rodriguez, EJ, Cannon, D, Caseras, X, Chaim-Avancini, TM, Elvsashagen, T, Favre, P, Foley, SF, Fullerton, JM, Goikolea, JM, Haarman, BCM, Hajek, T, Henry, C, Houenou, J, Howells, FM, Ingvar, M, Kuplicki, R, Lafer, B, Landen, M, Machado-Vieira, R, Malt, UF, McDonald, C, Mitchell, PB, Nabulsi, L, Otaduy, MCG, Overs, BJ, Polosan, M, Pomarol-Clotet, E, Radua, J, Rive, MM, Roberts, G, Ruhe, HG, Salvador, R, Sarro, S, Satterthwaite, TD, Savitz, J, Schene, AH, Schofield, PR, Serpa, MH, Sim, K, Soeiro-de-Souza, MG, Sutherland, AN, Temmingh, HS, Timmons, GM, Uhlmann, A, Vieta, E, Wolf, DH, Zanetti, MV, Jahanshad, N, Thompson, PM, Veltman, DJ, Penninx, BWJH, Marquand, AF, Cole, JH, and Schmaal, L

Abstract

Major depressive disorder (MDD) is associated with an increased risk of brain atrophy, aging-related diseases, and mortality. We examined potential advanced brain aging in adult MDD patients, and whether this process is associated with clinical characteristics in a large multicenter international dataset. We performed a mega-analysis by pooling brain measures derived from T1-weighted MRI scans from 19 samples worldwide. Healthy brain aging was estimated by predicting chronological age (18-75 years) from 7 subcortical volumes, 34 cortical thickness and 34 surface area, lateral ventricles and total intracranial volume measures separately in 952 male and 1236 female controls from the ENIGMA MDD working group. The learned model coefficients were applied to 927 male controls and 986 depressed males, and 1199 female controls and 1689 depressed females to obtain independent unbiased brain-based age predictions. The difference between predicted "brain age" and chronological age was calculated to indicate brain-predicted age difference (brain-PAD). On average, MDD patients showed a higher brain-PAD of +1.08 (SE 0.22) years (Cohen's d = 0.14, 95% CI: 0.08-0.20) compared with controls. However, this difference did not seem to be driven by specific clinical characteristics (recurrent status, remission status, antidepressant medication use, age of onset, or symptom severity). This highly powered collaborative effort showed subtle patterns of age-related structural brain abnormalities in MDD. Substantial within-group variance and overlap between groups were observed. Longitudinal studies of MDD and somatic health outcomes are needed to further assess the clinical value of these brain-PAD estimates.

Published
2021

8. Severity of current depression and remission status are associated with structural connectome alterations in major depressive disorder

Author

Repple, J, Mauritz, M, Meinert, S, de Lange, SC, Grotegerd, D, Opel, N, Redlich, R, Hahn, T, Foerster, K, Leehr, EJ, Winter, N, Goltermann, J, Enneking, V, Fingas, SM, Lemke, H, Waltemate, L, Nenadic, I, Krug, A, Brosch, K, Schmitt, S, Stein, F, Meller, T, Jansen, A, Steinstraeter, O, Baune, BT, Kircher, T, Dannlowski, U, van den Heuvel, MP, Repple, J, Mauritz, M, Meinert, S, de Lange, SC, Grotegerd, D, Opel, N, Redlich, R, Hahn, T, Foerster, K, Leehr, EJ, Winter, N, Goltermann, J, Enneking, V, Fingas, SM, Lemke, H, Waltemate, L, Nenadic, I, Krug, A, Brosch, K, Schmitt, S, Stein, F, Meller, T, Jansen, A, Steinstraeter, O, Baune, BT, Kircher, T, Dannlowski, U, and van den Heuvel, MP

Abstract

Major depressive disorder (MDD) is associated to affected brain wiring. Little is known whether these changes are stable over time and hence might represent a biological predisposition, or whether these are state markers of current disease severity and recovery after a depressive episode. Human white matter network ("connectome") analysis via network science is a suitable tool to investigate the association between affected brain connectivity and MDD. This study examines structural connectome topology in 464 MDD patients (mean age: 36.6 years) and 432 healthy controls (35.6 years). MDD patients were stratified categorially by current disease status (acute vs. partial remission vs. full remission) based on DSM-IV criteria. Current symptom severity was assessed continuously via the Hamilton Depression Rating Scale (HAMD). Connectome matrices were created via a combination of T1-weighted magnetic resonance imaging (MRI) and tractography methods based on diffusion-weighted imaging. Global tract-based metrics were not found to show significant differences between disease status groups, suggesting conserved global brain connectivity in MDD. In contrast, reduced global fractional anisotropy (FA) was observed specifically in acute depressed patients compared to fully remitted patients and healthy controls. Within the MDD patients, FA in a subnetwork including frontal, temporal, insular, and parietal nodes was negatively associated with HAMD, an effect remaining when correcting for lifetime disease severity. Therefore, our findings provide new evidence of MDD to be associated with structural, yet dynamic, state-dependent connectome alterations, which covary with current disease severity and remission status after a depressive episode.

Published
2020

9. ENIGMA MDD: seven years of global neuroimaging studies of major depression through worldwide data sharing

Author

Schmaal, L, Pozzi, E, Ho, TC, van Velzen, LS, Veer, IM, Opel, N, Van Someren, EJW, Han, LKM, Aftanas, L, Aleman, A, Baune, BT, Berger, K, Blanken, TF, Capitao, L, Couvy-Duchesne, B, Cullen, KR, Dannlowski, U, Davey, C, Erwin-Grabner, T, Evans, J, Frodl, T, Fu, CHY, Godlewska, B, Gotlib, IH, Goya-Maldonado, R, Grabe, HJ, Groenewold, NA, Grotegerd, D, Gruber, O, Gutman, BA, Hall, GB, Harrison, BJ, Hatton, SN, Hermesdorf, M, Hickie, IB, Hilland, E, Irungu, B, Jonassen, R, Kelly, S, Kircher, T, Klimes-Dougan, B, Krug, A, Landro, NI, Lagopoulos, J, Leerssen, J, Li, M, Linden, DEJ, MacMaster, FP, McIntosh, AM, Mehler, DMA, Nenadic, I, Penninx, BWJH, Portella, MJ, Reneman, L, Renteria, ME, Sacchet, MD, Saemann, PG, Schrantee, A, Sim, K, Soares, JC, Stein, DJ, Tozzi, L, van Der Wee, NJA, van Tol, M-J, Vermeiren, R, Vives-Gilabert, Y, Walter, H, Walter, M, Whalley, HC, Wittfeld, K, Whittle, S, Wright, MJ, Yang, TT, Zarate, C, Thomopoulos, SI, Jahanshad, N, Thompson, PM, Veltman, DJ, Schmaal, L, Pozzi, E, Ho, TC, van Velzen, LS, Veer, IM, Opel, N, Van Someren, EJW, Han, LKM, Aftanas, L, Aleman, A, Baune, BT, Berger, K, Blanken, TF, Capitao, L, Couvy-Duchesne, B, Cullen, KR, Dannlowski, U, Davey, C, Erwin-Grabner, T, Evans, J, Frodl, T, Fu, CHY, Godlewska, B, Gotlib, IH, Goya-Maldonado, R, Grabe, HJ, Groenewold, NA, Grotegerd, D, Gruber, O, Gutman, BA, Hall, GB, Harrison, BJ, Hatton, SN, Hermesdorf, M, Hickie, IB, Hilland, E, Irungu, B, Jonassen, R, Kelly, S, Kircher, T, Klimes-Dougan, B, Krug, A, Landro, NI, Lagopoulos, J, Leerssen, J, Li, M, Linden, DEJ, MacMaster, FP, McIntosh, AM, Mehler, DMA, Nenadic, I, Penninx, BWJH, Portella, MJ, Reneman, L, Renteria, ME, Sacchet, MD, Saemann, PG, Schrantee, A, Sim, K, Soares, JC, Stein, DJ, Tozzi, L, van Der Wee, NJA, van Tol, M-J, Vermeiren, R, Vives-Gilabert, Y, Walter, H, Walter, M, Whalley, HC, Wittfeld, K, Whittle, S, Wright, MJ, Yang, TT, Zarate, C, Thomopoulos, SI, Jahanshad, N, Thompson, PM, and Veltman, DJ

Abstract

A key objective in the field of translational psychiatry over the past few decades has been to identify the brain correlates of major depressive disorder (MDD). Identifying measurable indicators of brain processes associated with MDD could facilitate the detection of individuals at risk, and the development of novel treatments, the monitoring of treatment effects, and predicting who might benefit most from treatments that target specific brain mechanisms. However, despite intensive neuroimaging research towards this effort, underpowered studies and a lack of reproducible findings have hindered progress. Here, we discuss the work of the ENIGMA Major Depressive Disorder (MDD) Consortium, which was established to address issues of poor replication, unreliable results, and overestimation of effect sizes in previous studies. The ENIGMA MDD Consortium currently includes data from 45 MDD study cohorts from 14 countries across six continents. The primary aim of ENIGMA MDD is to identify structural and functional brain alterations associated with MDD that can be reliably detected and replicated across cohorts worldwide. A secondary goal is to investigate how demographic, genetic, clinical, psychological, and environmental factors affect these associations. In this review, we summarize findings of the ENIGMA MDD disease working group to date and discuss future directions. We also highlight the challenges and benefits of large-scale data sharing for mental health research.

Published
2020

10. Brain structural correlates of insomnia severity in 1053 individuals with major depressive disorder: results from the ENIGMA MDD Working Group

Author

Leerssen, J, Blanken, TF, Pozzi, E, Jahanshad, N, Aftanas, L, Andreassen, OA, Baune, BT, Brack, I, Carballedo, A, Ching, CRK, Dannlowski, U, Dohm, K, Enneking, V, Filimonova, E, Fingas, SM, Frodl, T, Godlewska, BR, Goltermann, J, Gotlib, IH, Grotegerd, D, Gruber, O, Harris, MA, Hatton, SN, Hawkins, E, Hickie, IB, Jaworska, N, Kircher, T, Krug, A, Lagopoulos, J, Lemke, H, Li, M, MacMaster, FP, McIntosh, AM, McLellan, Q, Meinert, S, Mwangi, B, Nenadic, I, Osipov, E, Portella, MJ, Redlich, R, Repple, J, Sacchet, MD, Saemann, PG, Simulionyte, E, Soares, JC, Walter, M, Watanabe, N, Whalley, HC, Yueksel, D, Veltman, DJ, Thompson, PM, Schmaal, L, Van Someren, EJW, Leerssen, J, Blanken, TF, Pozzi, E, Jahanshad, N, Aftanas, L, Andreassen, OA, Baune, BT, Brack, I, Carballedo, A, Ching, CRK, Dannlowski, U, Dohm, K, Enneking, V, Filimonova, E, Fingas, SM, Frodl, T, Godlewska, BR, Goltermann, J, Gotlib, IH, Grotegerd, D, Gruber, O, Harris, MA, Hatton, SN, Hawkins, E, Hickie, IB, Jaworska, N, Kircher, T, Krug, A, Lagopoulos, J, Lemke, H, Li, M, MacMaster, FP, McIntosh, AM, McLellan, Q, Meinert, S, Mwangi, B, Nenadic, I, Osipov, E, Portella, MJ, Redlich, R, Repple, J, Sacchet, MD, Saemann, PG, Simulionyte, E, Soares, JC, Walter, M, Watanabe, N, Whalley, HC, Yueksel, D, Veltman, DJ, Thompson, PM, Schmaal, L, and Van Someren, EJW

Abstract

It has been difficult to find robust brain structural correlates of the overall severity of major depressive disorder (MDD). We hypothesized that specific symptoms may better reveal correlates and investigated this for the severity of insomnia, both a key symptom and a modifiable major risk factor of MDD. Cortical thickness, surface area and subcortical volumes were assessed from T1-weighted brain magnetic resonance imaging (MRI) scans of 1053 MDD patients (age range 13-79 years) from 15 cohorts within the ENIGMA MDD Working Group. Insomnia severity was measured by summing the insomnia items of the Hamilton Depression Rating Scale (HDRS). Symptom specificity was evaluated with correlates of overall depression severity. Disease specificity was evaluated in two independent samples comprising 2108 healthy controls, and in 260 clinical controls with bipolar disorder. Results showed that MDD patients with more severe insomnia had a smaller cortical surface area, mostly driven by the right insula, left inferior frontal gyrus pars triangularis, left frontal pole, right superior parietal cortex, right medial orbitofrontal cortex, and right supramarginal gyrus. Associations were specific for insomnia severity, and were not found for overall depression severity. Associations were also specific to MDD; healthy controls and clinical controls showed differential insomnia severity association profiles. The findings indicate that MDD patients with more severe insomnia show smaller surfaces in several frontoparietal cortical areas. While explained variance remains small, symptom-specific associations could bring us closer to clues on underlying biological phenomena of MDD.

Published
2020

11. Using structural MRI to identify bipolar disorders-13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group

Author

Nunes, A, Schnack, HG, Ching, CRK, Agartz, I, Akudjedu, TN, Alda, M, Alnaes, D, Alonso-Lana, S, Bauer, J, Baune, BT, Boen, E, del Mar Bonnin, C, Busatto, GF, Canales-Rodriguez, EJ, Cannon, DM, Caseras, X, Chaim-Avancini, TM, Dannlowski, U, Diaz-Zuluaga, AM, Dietsche, B, Nhat, TD, Duchesnay, E, Elvsashagen, T, Emden, D, Eyler, LT, Fatjo-Vilas, M, Favre, P, Foley, SF, Fullerton, JM, Glahn, DC, Goikolea, JM, Grotegerd, D, Hahn, T, Henry, C, Hibar, DP, Houenou, J, Howells, FM, Jahanshad, N, Kaufmann, T, Kenney, J, Kircher, TTJ, Krug, A, Lagerberg, T, Lenroot, RK, Lopez-Jaramillo, C, Machado-Vieira, R, Malt, UF, McDonald, C, Mitchell, PB, Mwangi, B, Nabulsi, L, Opel, N, Overs, BJ, Pineda-Zapata, JA, Pomarol-Clotet, E, Redlich, R, Roberts, G, Rosa, PG, Salvador, R, Satterthwaite, TD, Soares, JC, Stein, DJ, Temmingh, HS, Trappenberg, T, Uhlmann, A, van Haren, NEM, Vieta, E, Westlye, LT, Wolf, DH, Yueksel, D, Zanetti, M, Andreassen, OA, Thompson, PM, Hajek, T, Nunes, A, Schnack, HG, Ching, CRK, Agartz, I, Akudjedu, TN, Alda, M, Alnaes, D, Alonso-Lana, S, Bauer, J, Baune, BT, Boen, E, del Mar Bonnin, C, Busatto, GF, Canales-Rodriguez, EJ, Cannon, DM, Caseras, X, Chaim-Avancini, TM, Dannlowski, U, Diaz-Zuluaga, AM, Dietsche, B, Nhat, TD, Duchesnay, E, Elvsashagen, T, Emden, D, Eyler, LT, Fatjo-Vilas, M, Favre, P, Foley, SF, Fullerton, JM, Glahn, DC, Goikolea, JM, Grotegerd, D, Hahn, T, Henry, C, Hibar, DP, Houenou, J, Howells, FM, Jahanshad, N, Kaufmann, T, Kenney, J, Kircher, TTJ, Krug, A, Lagerberg, T, Lenroot, RK, Lopez-Jaramillo, C, Machado-Vieira, R, Malt, UF, McDonald, C, Mitchell, PB, Mwangi, B, Nabulsi, L, Opel, N, Overs, BJ, Pineda-Zapata, JA, Pomarol-Clotet, E, Redlich, R, Roberts, G, Rosa, PG, Salvador, R, Satterthwaite, TD, Soares, JC, Stein, DJ, Temmingh, HS, Trappenberg, T, Uhlmann, A, van Haren, NEM, Vieta, E, Westlye, LT, Wolf, DH, Yueksel, D, Zanetti, M, Andreassen, OA, Thompson, PM, and Hajek, T

Abstract

Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective biological markers, such as those based on brain imaging, could aid in clinical management of BD. Machine learning (ML) brings neuroimaging analyses to individual subject level and may potentially allow for their diagnostic use. However, fair and optimal application of ML requires large, multi-site datasets. We applied ML (support vector machines) to MRI data (regional cortical thickness, surface area, subcortical volumes) from 853 BD and 2167 control participants from 13 cohorts in the ENIGMA consortium. We attempted to differentiate BD from control participants, investigated different data handling strategies and studied the neuroimaging/clinical features most important for classification. Individual site accuracies ranged from 45.23% to 81.07%. Aggregate subject-level analyses yielded the highest accuracy (65.23%, 95% CI = 63.47-67.00, ROC-AUC = 71.49%, 95% CI = 69.39-73.59), followed by leave-one-site-out cross-validation (accuracy = 58.67%, 95% CI = 56.70-60.63). Meta-analysis of individual site accuracies did not provide above chance results. There was substantial agreement between the regions that contributed to identification of BD participants in the best performing site and in the aggregate dataset (Cohen's Kappa = 0.83, 95% CI = 0.829-0.831). Treatment with anticonvulsants and age were associated with greater odds of correct classification. Although short of the 80% clinically relevant accuracy threshold, the results are promising and provide a fair and realistic estimate of classification performance, which can be achieved in a large, ecologically valid, multi-site sample of BD participants based on regional neurostructural measures. Furthermore, the significant classification in different samples was based on plausible and similar neuroanatomical features. Future multi-site studies should move towards sharing of raw/voxelwise neuroimaging data.

Published
2020

12. Biological sex classification with structural MRI data shows increased misclassification in transgender women

Author

Flint, C, Foerster, K, Koser, SA, Konrad, C, Zwitserlood, P, Berger, K, Hermesdorf, M, Kircher, T, Krug, A, Baune, BT, Dohm, K, Redlich, R, Opel, N, Arolt, V, Hahn, T, Jiang, X, Dannlowski, U, Grotegerd, D, Nenadic, I, Flint, C, Foerster, K, Koser, SA, Konrad, C, Zwitserlood, P, Berger, K, Hermesdorf, M, Kircher, T, Krug, A, Baune, BT, Dohm, K, Redlich, R, Opel, N, Arolt, V, Hahn, T, Jiang, X, Dannlowski, U, Grotegerd, D, and Nenadic, I

Abstract

Transgender individuals (TIs) show brain-structural alterations that differ from their biological sex as well as their perceived gender. To substantiate evidence that the brain structure of TIs differs from male and female, we use a combined multivariate and univariate approach. Gray matter segments resulting from voxel-based morphometry preprocessing of N = 1753 cisgender (CG) healthy participants were used to train (N = 1402) and validate (20% holdout N = 351) a support-vector machine classifying the biological sex. As a second validation, we classified N = 1104 patients with depression. A third validation was performed using the matched CG sample of the transgender women (TW) application sample. Subsequently, the classifier was applied to N = 26 TW. Finally, we compared brain volumes of CG-men, women, and TW-pre/post treatment cross-sex hormone treatment (CHT) in a univariate analysis controlling for sexual orientation, age, and total brain volume. The application of our biological sex classifier to the transgender sample resulted in a significantly lower true positive rate (TPR-male = 56.0%). The TPR did not differ between CG-individuals with (TPR-male = 86.9%) and without depression (TPR-male = 88.5%). The univariate analysis of the transgender application-sample revealed that TW-pre/post treatment show brain-structural differences from CG-women and CG-men in the putamen and insula, as well as the whole-brain analysis. Our results support the hypothesis that brain structure in TW differs from brain structure of their biological sex (male) as well as their perceived gender (female). This finding substantiates evidence that TIs show specific brain-structural alterations leading to a different pattern of brain structure than CG-individuals.

Published
2020

13. White matter disturbances in major depressive disorder: a coordinated analysis across 20 international cohorts in the ENIGMA MDD working group

Author

van Velzen, LS, Kelly, S, Isaev, D, Aleman, A, Aftanas, L, Bauer, J, Baune, BT, Brak, I, Carballedo, A, Connolly, CG, Couvy-Duchesne, B, Cullen, KR, Danilenko, K, Dannlowski, U, Enneking, V, Filimonova, E, Foerster, K, Frodl, T, Gotlib, IH, Groenewold, NA, Grotegerd, D, Harris, MA, Hatton, SN, Hawkins, EL, Hickie, IB, Ho, TC, Jansen, A, Kircher, T, Klimes-Dougan, B, Kochunov, P, Krug, A, Lagopoulos, J, Lee, R, Lett, TA, Li, M, MacMaster, FP, Martin, NG, McIntosh, AM, McLellan, Q, Meinert, S, Nenadic, I, Osipov, E, Penninx, BWJH, Portella, MJ, Repple, J, Roos, A, Sacchet, MD, Saemann, PG, Schnell, K, Shen, X, Sim, K, Stein, DJ, van Tol, M-J, Tomyshev, AS, Tozzi, L, Veer, IM, Vermeiren, R, Vives-Gilabert, Y, Walter, H, Walter, M, van der Wee, NJA, van der Werff, SJA, Schreiner, MW, Whalley, HC, Wright, MJ, Yang, TT, Zhu, A, Veltman, DJ, Thompson, PM, Jahanshad, N, Schmaal, L, van Velzen, LS, Kelly, S, Isaev, D, Aleman, A, Aftanas, L, Bauer, J, Baune, BT, Brak, I, Carballedo, A, Connolly, CG, Couvy-Duchesne, B, Cullen, KR, Danilenko, K, Dannlowski, U, Enneking, V, Filimonova, E, Foerster, K, Frodl, T, Gotlib, IH, Groenewold, NA, Grotegerd, D, Harris, MA, Hatton, SN, Hawkins, EL, Hickie, IB, Ho, TC, Jansen, A, Kircher, T, Klimes-Dougan, B, Kochunov, P, Krug, A, Lagopoulos, J, Lee, R, Lett, TA, Li, M, MacMaster, FP, Martin, NG, McIntosh, AM, McLellan, Q, Meinert, S, Nenadic, I, Osipov, E, Penninx, BWJH, Portella, MJ, Repple, J, Roos, A, Sacchet, MD, Saemann, PG, Schnell, K, Shen, X, Sim, K, Stein, DJ, van Tol, M-J, Tomyshev, AS, Tozzi, L, Veer, IM, Vermeiren, R, Vives-Gilabert, Y, Walter, H, Walter, M, van der Wee, NJA, van der Werff, SJA, Schreiner, MW, Whalley, HC, Wright, MJ, Yang, TT, Zhu, A, Veltman, DJ, Thompson, PM, Jahanshad, N, and Schmaal, L

Abstract

Alterations in white matter (WM) microstructure have been implicated in the pathophysiology of major depressive disorder (MDD). However, previous findings have been inconsistent, partially due to low statistical power and the heterogeneity of depression. In the largest multi-site study to date, we examined WM anisotropy and diffusivity in 1305 MDD patients and 1602 healthy controls (age range 12-88 years) from 20 samples worldwide, which included both adults and adolescents, within the MDD Working Group of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) consortium. Processing of diffusion tensor imaging (DTI) data and statistical analyses were harmonized across sites and effects were meta-analyzed across studies. We observed subtle, but widespread, lower fractional anisotropy (FA) in adult MDD patients compared with controls in 16 out of 25 WM tracts of interest (Cohen's d between 0.12 and 0.26). The largest differences were observed in the corpus callosum and corona radiata. Widespread higher radial diffusivity (RD) was also observed (all Cohen's d between 0.12 and 0.18). Findings appeared to be driven by patients with recurrent MDD and an adult age of onset of depression. White matter microstructural differences in a smaller sample of adolescent MDD patients and controls did not survive correction for multiple testing. In this coordinated and harmonized multisite DTI study, we showed subtle, but widespread differences in WM microstructure in adult MDD, which may suggest structural disconnectivity in MDD.

Published
2020

14. Widespread white matter microstructural abnormalities in bipolar disorder: evidence from mega- and meta-analyses across 3033 individuals

Author

Favre, P, Pauling, M, Stout, J, Hozer, F, Sarrazin, S, Abe, C, Alda, M, Alloza, C, Alonso-Lana, S, Andreassen, OA, Baune, BT, Benedetti, F, Busatto, GF, Canales-Rodriguez, EJ, Caseras, X, Chaim-Avancini, TM, Ching, CRK, Dannlowski, U, Deppe, M, Eyler, LT, Fatjo-Vilas, M, Foley, SF, Grotegerd, D, Hajek, T, Haukvik, UK, Howells, FM, Jahanshad, N, Kugel, H, Lagerberg, TV, Lawrie, SM, Linke, JO, McIntosh, A, Melloni, EMT, Mitchell, PB, Polosan, M, Pomarol-Clotet, E, Repple, J, Roberts, G, Roos, A, Rosa, PGP, Salvador, R, Sarro, S, Schofield, PR, Serpa, MH, Sim, K, Stein, DJ, Sussmann, JE, Temmingh, HS, Thompson, PM, Verdolini, N, Vieta, E, Wessa, M, Whalley, HC, Zanetti, MV, Leboyer, M, Mangin, J-F, Henry, C, Duchesnay, E, Houenou, J, Favre, P, Pauling, M, Stout, J, Hozer, F, Sarrazin, S, Abe, C, Alda, M, Alloza, C, Alonso-Lana, S, Andreassen, OA, Baune, BT, Benedetti, F, Busatto, GF, Canales-Rodriguez, EJ, Caseras, X, Chaim-Avancini, TM, Ching, CRK, Dannlowski, U, Deppe, M, Eyler, LT, Fatjo-Vilas, M, Foley, SF, Grotegerd, D, Hajek, T, Haukvik, UK, Howells, FM, Jahanshad, N, Kugel, H, Lagerberg, TV, Lawrie, SM, Linke, JO, McIntosh, A, Melloni, EMT, Mitchell, PB, Polosan, M, Pomarol-Clotet, E, Repple, J, Roberts, G, Roos, A, Rosa, PGP, Salvador, R, Sarro, S, Schofield, PR, Serpa, MH, Sim, K, Stein, DJ, Sussmann, JE, Temmingh, HS, Thompson, PM, Verdolini, N, Vieta, E, Wessa, M, Whalley, HC, Zanetti, MV, Leboyer, M, Mangin, J-F, Henry, C, Duchesnay, E, and Houenou, J

Abstract

Fronto-limbic white matter (WM) abnormalities are assumed to lie at the heart of the pathophysiology of bipolar disorder (BD); however, diffusion tensor imaging (DTI) studies have reported heterogeneous results and it is not clear how the clinical heterogeneity is related to the observed differences. This study aimed to identify WM abnormalities that differentiate patients with BD from healthy controls (HC) in the largest DTI dataset of patients with BD to date, collected via the ENIGMA network. We gathered individual tensor-derived regional metrics from 26 cohorts leading to a sample size of N = 3033 (1482 BD and 1551 HC). Mean fractional anisotropy (FA) from 43 regions of interest (ROI) and average whole-brain FA were entered into univariate mega- and meta-analyses to differentiate patients with BD from HC. Mega-analysis revealed significantly lower FA in patients with BD compared with HC in 29 regions, with the highest effect sizes observed within the corpus callosum (R2 = 0.041, Pcorr < 0.001) and cingulum (right: R2 = 0.041, left: R2 = 0.040, Pcorr < 0.001). Lithium medication, later onset and short disease duration were related to higher FA along multiple ROIs. Results of the meta-analysis showed similar effects. We demonstrated widespread WM abnormalities in BD and highlighted that altered WM connectivity within the corpus callosum and the cingulum are strongly associated with BD. These brain abnormalities could represent a biomarker for use in the diagnosis of BD. Interactive three-dimensional visualization of the results is available at www.enigma-viewer.org.

Published
2019

15. Cortical abnormalities in bipolar disorder: an MRI analysis of 6503 individuals from the ENIGMA Bipolar Disorder Working Group

Author

Hibar, DP, Westlye, LT, Doan, NT, Jahanshad, N, Cheung, JW, Ching, CRK, Versace, A, Bilderbeck, AC, Uhlmann, A, Mwangi, B, Kraemer, B, Overs, B, Hartberg, CB, Abe, C, Dima, D, Grotegerd, D, Sprooten, E, Boen, E, Jimenez, E, Howells, FM, Delvecchio, G, Temmingh, H, Starke, J, Almeida, JRC, Goikolea, JM, Houenou, J, Beard, LM, Rauer, L, Abramovic, L, Bonnin, M, Ponteduro, MF, Keil, M, Rive, MM, Yao, N, Yalin, N, Najt, P, Rosa, PG, Redlich, R, Trost, S, Hagenaars, S, Fears, SC, Alonso-Lana, S, van Erp, TGM, Nickson, T, Chaim-Avancini, TM, Meier, TB, Elvsashagen, T, Haukvik, UK, Lee, WH, Schene, AH, Lloyd, AJ, Young, AH, Nugent, A, Dale, AM, Pfennig, A, McIntosh, AM, Lafer, B, Baune, BT, Ekman, CJ, Zarate, CA, Bearden, CE, Henry, C, Simhandl, C, McDonald, C, Bourne, C, Stein, DJ, Wolf, DH, Cannon, DM, Glahn, DC, Veltman, DJ, Pomarol-Clotet, E, Vieta, E, Canales-Rodriguez, EJ, Nery, FG, Duran, FLS, Busatto, GF, Roberts, G, Pearlson, GD, Goodwin, GM, Kugel, H, Whalley, HC, Ruhe, HG, Soares, JC, Fullerton, JM, Rybakowski, JK, Savitz, J, Chaim, KT, Fatjo-Vilas, M, Soeiro-de-Souza, MG, Boks, MP, Zanetti, MV, Otaduy, MCG, Schaufelberger, MS, Alda, M, Ingvar, M, Phillips, ML, Kempton, MJ, Bauer, M, Landen, M, Lawrence, NS, van Haren, NEM, Horn, NR, Freimer, NB, Gruber, O, Schofield, PR, Mitchell, PB, Kahn, RS, Lenroot, R, Machado-Vieira, R, Ophoff, RA, Sarro, S, Frangou, S, Satterthwaite, TD, Hajek, T, Dannlowski, U, Malt, UF, Arolt, V, Gattaz, WF, Drevets, WC, Caseras, X, Agartz, I, Thompson, PM, Andreassen, OA, Hibar, DP, Westlye, LT, Doan, NT, Jahanshad, N, Cheung, JW, Ching, CRK, Versace, A, Bilderbeck, AC, Uhlmann, A, Mwangi, B, Kraemer, B, Overs, B, Hartberg, CB, Abe, C, Dima, D, Grotegerd, D, Sprooten, E, Boen, E, Jimenez, E, Howells, FM, Delvecchio, G, Temmingh, H, Starke, J, Almeida, JRC, Goikolea, JM, Houenou, J, Beard, LM, Rauer, L, Abramovic, L, Bonnin, M, Ponteduro, MF, Keil, M, Rive, MM, Yao, N, Yalin, N, Najt, P, Rosa, PG, Redlich, R, Trost, S, Hagenaars, S, Fears, SC, Alonso-Lana, S, van Erp, TGM, Nickson, T, Chaim-Avancini, TM, Meier, TB, Elvsashagen, T, Haukvik, UK, Lee, WH, Schene, AH, Lloyd, AJ, Young, AH, Nugent, A, Dale, AM, Pfennig, A, McIntosh, AM, Lafer, B, Baune, BT, Ekman, CJ, Zarate, CA, Bearden, CE, Henry, C, Simhandl, C, McDonald, C, Bourne, C, Stein, DJ, Wolf, DH, Cannon, DM, Glahn, DC, Veltman, DJ, Pomarol-Clotet, E, Vieta, E, Canales-Rodriguez, EJ, Nery, FG, Duran, FLS, Busatto, GF, Roberts, G, Pearlson, GD, Goodwin, GM, Kugel, H, Whalley, HC, Ruhe, HG, Soares, JC, Fullerton, JM, Rybakowski, JK, Savitz, J, Chaim, KT, Fatjo-Vilas, M, Soeiro-de-Souza, MG, Boks, MP, Zanetti, MV, Otaduy, MCG, Schaufelberger, MS, Alda, M, Ingvar, M, Phillips, ML, Kempton, MJ, Bauer, M, Landen, M, Lawrence, NS, van Haren, NEM, Horn, NR, Freimer, NB, Gruber, O, Schofield, PR, Mitchell, PB, Kahn, RS, Lenroot, R, Machado-Vieira, R, Ophoff, RA, Sarro, S, Frangou, S, Satterthwaite, TD, Hajek, T, Dannlowski, U, Malt, UF, Arolt, V, Gattaz, WF, Drevets, WC, Caseras, X, Agartz, I, Thompson, PM, and Andreassen, OA

Abstract

Despite decades of research, the pathophysiology of bipolar disorder (BD) is still not well understood. Structural brain differences have been associated with BD, but results from neuroimaging studies have been inconsistent. To address this, we performed the largest study to date of cortical gray matter thickness and surface area measures from brain magnetic resonance imaging scans of 6503 individuals including 1837 unrelated adults with BD and 2582 unrelated healthy controls for group differences while also examining the effects of commonly prescribed medications, age of illness onset, history of psychosis, mood state, age and sex differences on cortical regions. In BD, cortical gray matter was thinner in frontal, temporal and parietal regions of both brain hemispheres. BD had the strongest effects on left pars opercularis (Cohen's d=-0.293; P=1.71 × 10-21), left fusiform gyrus (d=-0.288; P=8.25 × 10-21) and left rostral middle frontal cortex (d=-0.276; P=2.99 × 10-19). Longer duration of illness (after accounting for age at the time of scanning) was associated with reduced cortical thickness in frontal, medial parietal and occipital regions. We found that several commonly prescribed medications, including lithium, antiepileptic and antipsychotic treatment showed significant associations with cortical thickness and surface area, even after accounting for patients who received multiple medications. We found evidence of reduced cortical surface area associated with a history of psychosis but no associations with mood state at the time of scanning. Our analysis revealed previously undetected associations and provides an extensive analysis of potential confounding variables in neuroimaging studies of BD.

Published
2018

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